Assessment of Personal Health Care Management and Chronic Disease Prevalence: Comparative Analysis of Demographic, Socioeconomic, and Health-Related Variables.

BACKGROUND: The use of personal health care management (PHM) is increasing rapidly within the United States because of implementation of health technology across the health care continuum and increased regulatory requirements for health care providers and organizations promoting the use of PHM, particularly the use of text messaging (short message service), Web-based scheduling, and Web-based requests for prescription renewals. Limited research has been conducted comparing PHM use across groups based on chronic conditions. OBJECTIVE: This study aimed to describe the overall utilization of PHM and compare individual characteristics associated with PHM in groups with no reported chronic conditions, with 1 chronic condition, and with 2 or more such conditions. METHODS: Datasets drawn from the National Health Interview Survey were analyzed using multiple logistic regression to determine the level of PHM use in relation to demographic, socioeconomic, or health-related factors. Data from 47,814 individuals were analyzed using logistic regression. RESULTS: Approximately 12.19% (5737/47,814) of respondents reported using PHM, but higher rates of use were reported by individuals with higher levels of education and income. The overall rate of PHM remained stable between 2009 and 2014, despite increased focus on the promotion of patient engagement initiatives. Demographic factors predictive of PHM use included people who were younger, non-Hispanic, and who lived in the western region of the United States. There were also differences in PHM use based on socioeconomic factors. Respondents with college-level education were over 2.5 times more likely to use PHM than respondents without college-level education. Health-related factors were also predictive of PHM use. Individuals with health insurance and a usual place for health care were more likely to use PHM than individuals with no health insurance and no usual place for health care. Individuals reporting a single chronic condition or multiple chronic conditions reported slightly higher levels of PHM use than individuals reporting no chronic conditions. Individuals with no chronic conditions who did not experience barriers to accessing health care were more likely to use PHM than individuals with 1 or more chronic conditions. CONCLUSIONS: The findings of this study illustrated the disparities in PHM use based on the number of chronic conditions and that multiple factors influence the use of PHM, including economics and education. These findings provide evidence of the challenge associated with engaging patients using electronic health information as the health care industry continues to evolve.

Discovering and identifying New York heart association classification from electronic health records.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes. METHODS: We accessed HF patients' data in a local EHR system and identified potential sources of NYHA, including local diagnosis codes, procedures, and clinical notes. We further investigated and compared the performances of rule-based versus machine learning-based natural language processing (NLP) methods to identify NYHA class from clinical notes. RESULTS: Of the 36,276 patients with a diagnosis of HF or a CRT implant, 19.2% had NYHA class mentioned at least once in their EHR. While NYHA class existed in descriptive fields association with diagnosis codes (31%) or procedure codes (2%), the richest source of NYHA class was clinical notes (95%). A total of 6174 clinical notes were matched with hospital-specific custom NYHA class diagnosis codes. Machine learning-based methods outperformed a rule-based method. The best machine-learning method was a random forest with n-gram features (F-measure: 93.78%). CONCLUSIONS: NYHA class is documented in different parts in EHR for HF patients and the documentation rate is lower than expected. NLP methods are a feasible way to extract NYHA class information from clinical notes.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials.

BACKGROUND: An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. METHODS: Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. RESULTS: Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). CONCLUSIONS: Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.

Lead Testing in a Pediatric Population: Underscreening and Problematic Repeated Tests.

CONTEXT: Underscreening and problematic repeat lead testing in children. OBJECTIVE: Identify proportion of underscreening for elevated blood-lead levels in children. For children who receive a lead test, measure the level of problematic repeat lead tests, defined as those with a high probability of not meeting recommended guidelines for lead testing in children measured using a combination of patients' age, test type and sequencing, days between tests, and encounter diagnosis coding. DESIGN: A population-based retrospective cross-sectional design. SETTING: All health care services organizations in the state of Minnesota that delivered health services to the defined study population. PARTICIPANTS: The study population was a Medicaid cohort of 12 436 children aged 0 to 18 years observed over a 1-year period. MAIN OUTCOME MEASURES: Proportion of eligible children not receiving at least 1 lead test; proportion of problematic repeat lead tests. RESULTS: Thirty-five percent of children who should have received at least 1 lead test (n = 1714) during the study period did not. A total of 1856 children had at least 1 lead test and 190 had 2 or more. Fifty percent (50%) of the repeat tests were identified as problematic, representing 5.1% of the lead tests performed. Repeat tests performed in different health systems than the systems where the initial tests were performed had 5.3 times greater odds (adjusted odds ratio: 5.3 [95% confidence interval, 2.8-9.9]) of being problematic. CONCLUSIONS: The current approach to delivering mandatory lead testing across the state Medicaid population does not ensure that children are appropriately tested and has potential inefficiencies in that testing when it does take place. Use of multiple health care systems is associated with increased potential inefficiencies. Future Medicaid accountable care agreements between the state Medicaid program and participating health systems should emphasize clear population accountability for test screenings to improve patients' safety. A central queryable health resource or health information exchange may enable this.

Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BACKGROUND: Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a "Learning Healthcare System" at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. METHODS: Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. RESULTS: The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. CONCLUSIONS: Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

The Relationship Between Patient Perceived Pain and the Numerical Rating System: A Prospective Validation Using Electronic Health Record Data.

We developed a method of using the Clinically Aligned Pain Assessment (CAPA) measures to reconstruct the Numeric Rating System (NRS). We used an observational retrospective cohort study design with prospective validation using de-identified adult patient data derived from a major health system. Data between 2011-2017 were used for development and 2018-2020 for validation. All included patients had at least one NRS and CAPA measurement at the same time. An ordinal regression model was built with CAPA components to predict NRS scores. We identified 6,414 and 3,543 simultaneous NRS-CAPA pairs in the development and validation dataset, respectively. All CAPA components were significantly related to NRS, with RMSE of 1.938 and Somers' D of 0.803 on the development dataset, and RMSE of 2.1 and Somers' D of 0.74 when prospectively validated. Our model was capable of accurately reconstructing NRS based on CAPA and was exact when the NRS was [0,7].

Effects of time constraints on clinician-computer interaction: a study on information synthesis from EHR clinical notes.

BACKGROUND: Time is a measurable and critical resource that affects the quality of services provided in clinical practice. There is limited insight into the effects of time restrictions on clinicians' cognitive processes with the electronic health record (EHR) in providing ambulatory care. OBJECTIVE: To understand the impact of time constraints on clinicians' synthesis of text-based EHR clinical notes. METHODS: We used an established clinician cognitive framework based on a think-aloud protocol. We studied interns' thought processes as they accomplished a set of four preformed ambulatory care clinical scenarios with and without time restrictions in a controlled setting. RESULTS: Interns most often synthesized details relevant to patients' problems and treatment, regardless of whether or not the time available for task performance was restricted. In contrast to previous findings, subsequent information commonly synthesized by clinicians related most commonly to the chronology of clinical events for the unrestricted time observations and to investigative procedures for the time-restricted sessions. There was no significant difference in the mean number of omission errors and incorrect deductions when interns synthesized the EHR clinical notes with and without time restrictions (3.5±0.5 vs. 2.3±0.5, p=0.14). CONCLUSION: Our results suggest that the incidence of errors during clinicians' synthesis of EHR clinical notes is not increased with modest time restrictions, possibly due to effective adjustments of information processing strategies learned from the usual time-constrained nature of patient visits. Further research is required to investigate the effects of similar or more extreme time variations on cognitive processes employed with different levels of expertise, specialty, and with different care settings.

Does initial length of stay impact 30-day readmission risk in pediatric asthma patients?

OBJECTIVES: Accountable care puts pressure on hospitals to manage care episodes. Initial length of stay (ILOS) and readmission risk are important elements of a care episode and measures of care quality. Understanding the association between these two measures can guide hospital efforts in managing care episodes. This study was designed to explore the association between ILOS and readmission risk in a cohort of pediatric asthma patients. MATERIALS AND METHODS: The sample cohort (n = 4965) consisted of all asthma patients discharged from Children's Hospitals and Clinics of Minnesota (CHC MN) from January 2008 through August 2012. Asthma discharges included cases with a principal diagnosis of asthma or certain respiratory cases with asthma listed as a secondary diagnosis. Multiple logistic regression was used to test associations, adjusting for covariates. RESULTS: Adjusting for covariates, we found no significant association between ILOS and readmission (OR: 1.04 [95% CI: 0.98-1.10]). Analyzing ILOS categorically by length of stay, one-day stays did not have a significantly higher readmission risk (OR:1.27 [95% CI: 0.87-1.85]) than two-day stays, which had the lowest observed readmission risk. Risk increased as ILOS exceeded two days but was not significantly different by day. We found no association when comparing the difference in actual versus expected ILOS and readmission risk (shorter than expected OR: 1.13 [95% CI: 0.74-1.71]; longer than expected OR: 0.97 [95% CI: 0.69-1.38]). CONCLUSIONS: Attempts to prolong ILOS would dramatically increase costs with little impact on readmissions. For example, increasing one-day visits to two-day visits would increase hospital patient days 38% (1870 d) in this cohort while decreasing total readmissions by 3.8% [95% CI: 3.6-4.0%]. Understanding the mechanisms that impact readmissions is essential in evaluating cost-effective approaches to improving patient outcomes and lowering the cost of care.

A model for the electronic support of practice-based research networks.

PURPOSE: The principal goal of the electronic Primary Care Research Network (ePCRN) is to enable the development of an electronic infrastructure to support clinical research activities in primary care practice-based research networks (PBRNs). We describe the model that the ePCRN developed to enhance the growth and to expand the reach of PBRN research. METHODS: Use cases and activity diagrams were developed from interviews with key informants from 11 PBRNs from the United States and United Kingdom. Discrete functions were identified and aggregated into logical components. Interaction diagrams were created, and an overall composite diagram was constructed describing the proposed software behavior. Software for each component was written and aggregated, and the resulting prototype application was pilot tested for feasibility. A practical model was then created by separating application activities into distinct software packages based on existing PBRN business rules, hardware requirements, network requirements, and security concerns. RESULTS: We present an information architecture that provides for essential interactions, activities, data flows, and structural elements necessary for providing support for PBRN translational research activities. The model describes research information exchange between investigators and clusters of independent data sites supported by a contracted research director. The model was designed to support recruitment for clinical trials, collection of aggregated anonymous data, and retrieval of identifiable data from previously consented patients across hundreds of practices. CONCLUSIONS: The proposed model advances our understanding of the fundamental roles and activities of PBRNs and defines the information exchange commonly used by PBRNs to successfully engage community health care clinicians in translational research activities. By describing the network architecture in a language familiar to that used by software developers, the model provides an important foundation for the development of electronic support for essential PBRN research activities.

Envisioning a learning health care system: the electronic primary care research network, a case study.

PURPOSE: The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the U.S. National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). METHODS: We identified the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within U.S. practice-based research networks. RESULTS: We developed a system to assist in the identification of eligible cohorts from EHR data. To preserve privacy, counts and flagging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. CONCLUSIONS: A means of accessing health care data for research is not sufficient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.

Patient-centered applications: use of information technology to promote disease management and wellness. A white paper by the AMIA knowledge in motion working group.

Advances in information technology (IT) enable a fundamental redesign of health care processes based on the use and integration of electronic communication at all levels. New communication technologies can support a transition from institution centric to patient-centric applications. This white paper defines key principles and challenges for designers, policy makers, and evaluators of patient-centered technologies for disease management and prevention. It reviews current and emerging trends; highlights challenges related to design, evaluation, reimbursement and usability; and reaches conclusions for next steps that will advance the domain.

Telehealth: the promise of new care delivery models.

Telehealth possesses a significant potential to revolutionize healthcare delivery processes by challenging some of the long-held assumptions about healthcare delivery and by creating innovative alternative models. Those assumptions relate to the location-linked nature of healthcare and its episodic nature. Telehealth can challenge the assumption that healthcare is inextricably linked to the provider's location. Numerous models involving such approaches as interactive videoconferencing and store-and-forward technologies already exist. Telehealth also challenges the episodic nature of care. One example is provided by the models evolving from the convergence of three technologies: remote monitoring, electronic health records, and clinical decision support systems. Telehealth-based models of care can also lead to a reduced demand for services and greater efficiencies in the care process. These telehealth-enabled care delivery models have the potential to reduce the costs of care, improve quality, and mitigate provider shortages. However, the achievement of these goals is not straightforward. The current healthcare financing system is not designed to support such new models, and the existing healthcare culture is deeply ingrained within workflow processes and provider attitudes. A great deal of work remains to be done before the benefits of telehealth-based care delivery models are fully realized. Change is inherently risky but we must have the courage to assume the risk in order to create telehealth-driven innovations that lead to better and more cost-effective medical care for all.

A time-motion study to evaluate the impact of EMR and CPOE implementation on physician efficiency.

The objective of this research is to determine the impact of EMR and CPOE implementation on physician efficiency. A time series observational study was conducted within a hospital setting at six weeks pre-implementation, six weeks post-implementation and five months post-implementation. All 19 subjects were observed twice with one patient per observation. Physician follow-up rounding times per patient were measured. Physicians demonstrated a mean total rounding time of 18.79 minutes (pre-implementation); 16.97 minutes (six weeks post-implementation); and 12.97 minutes (five months post). Overall, the results showed a statistically significant F value = 8.26 > 1 (p = 0.0011) that signifies a reduction in physician rounding time within the hospital setting following EMR implementation. Results also showed overall standard deviations of 6.96 minutes (pre-implementation); 5.13 minutes (six weeks post); and 3.69 minutes (five months post), possibly signifying a reduction in variability and a narrower distribution of rounding times with increased similarity in physicians' rounding patterns.

A mHealth-based care model for improving hypertension control in stroke survivors: Pilot RCT.

PURPOSE: Hypertension (HTN) is significantly under-treated in stroke survivors. We examined usability and efficacy of a mHealth -based care model for improving post-stroke HTN control (Funding: AHRQ R21HS021794). METHODS: We used a RCT design. Planned study duration was 90 days. Intervention arm (IA) participants measured their BP daily using a smart phone and wireless BP monitor. This was transmitted automatically to the study database. Investigators (Physician + PharmD) made bi-weekly medication adjustments to achieve the BP goal. Control arm (CA) participants received a digital BP monitor and usual care. We examined Usability (measured with Marshfield System Usability Survey) and HTN control efficacy using an ITT (intent-to-treat) and as-treated (AT) analyses. RESULTS: Fifty participants (IA = 28; CA = 22) completed the study. The Marshfield survey question, "I thought the system was easy to use" mean score was 4.6, (5 = strongly agree). Mean SBP declined significantly between enrollment and study completion in the IA. In ITT, IA SBP declined 9.88 mm, p = 0.005. In AT, IA SBP declined 10.81 mm, p = 0.0036. CA SBP decline was 5-6 mm Hg (not significant). In the ITT, baseline HTN control (SBP < 140 mm Hg) was 50% in IA and CA. At study completion, HTN was controlled in 82% (23/28) of IA and 64% (14/22) of CA (p = 0.14). In the AT, HTN was controlled in 89% (23/26) of IA and 58% (14/24) of CA, (p = 0.015). CONCLUSION: A mHealth-based HTN care model had excellent usability and provided better HTN control than usual care in stroke survivors. CLINICAL TRIAL: gov: NCT01875094.

Multiple Factors Drive Opioid Prescribing at the Time of Discharge.

Multiple factors potentially influence pain intensity or frequency, and consequently the need for an opioid prescription. This study aims to identify factors associated with being discharged with an outpatient opioid prescription. We constructed a database containing clinical, non-clinical, and organizational variables from the EHR that are potentially relevant for ordering an opioid at discharge. Descriptive statistics of these variables and univariate association analysis reveal that all of the examined variables to be statistically significantly associated with opioid prescription at discharge. Further, we fitted a random forest model to examine the information content in the examined variables regarding whether a patient will be discharged with an opioid. The model resulted in a mean AUC of 0.84, suggesting the factors examined in this study in combination contain significant information regarding prescription of an opioid at discharge.

Penetration and adoption of health information technology (IT) in Thailand's community health centers (CHCs): a national survey.

A universal healthcare coverage program has been implemented in Thailand since 2001 and the Thailand Ministry of Public Health (MOPH) is restructuring its health information systems to support the management of this reform. The MOPH believes that health information technology (IT) is fundamental to the development of an effective health information system, and that users' adoption of health IT is one of the most important factors to the success of health IT implementation projects. However, there is no national data available regarding the penetration and adoption of health IT in Thai community health centers (CHCs). This cross sectional survey was designed to study the penetration and adoption of health IT in the country's community health centers. A random sample of 1,607 regionally stratified CHC's from a total of 9,806 CHCs was selected. With an 82% response rate, the data showed that people who worked in CHCs were currently heavy users of health IT. They exhibited high IT acceptance and positive attitudes toward using health IT. CHCs' staff was less resistant to adopt health IT than previously anticipated. These results are similar in all of the country's geographic regions. Health IT is pervasive in CHCs across the country and penetrates all regions.

Automatic Methods to Extract New York Heart Association Classification from Clinical Notes.

Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) classification is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. However, NYHA is rarely stored in EHR structured data such information is often documented in unstructured clinical notes. In this study, we thus investigated the use of natural language processing (NLP) methods to identify NYHA classification from clinical notes. We collected 6,174 clinical notes that were matched with hospital-specific custom NYHA class diagnosis codes. Machine-learning based methods performed similar with a rule-based method. The best machine-learning method, support vector machine with n-gram features, performed the best (93% F-measure). Further validation of the findings is required.

Quantifying the Effect of Data Quality on the Validity of an eMeasure.

Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178­urinary catheter removal after surgery). Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue. Results Catheter duration had the most impact on the CMS178 eMeasure with every 1% reduction in data quality causing a 1.21% increase in the number of missing events. For birth date and admission type, every 1% reduction in data quality resulted in a 1% increase in missing events. Conclusion This research demonstrated that the impact of data quality issues can be quantified using a generalized process and that the CMS178 eMeasure, as currently defined, may not measure how well an organization is meeting the intended best practice goal. Secondary use of EHR data is warranted only if the data are of sufficient quality. The assessment approach described in this study demonstrates how the impact of data quality issues on an eMeasure can be quantified and the approach can be generalized for other data analysis tasks. Healthcare organizations can prioritize data quality improvement efforts to focus on the areas that will have the most impact on validity and assess whether the values that are reported should be trusted.

Telehealth and the national health information technology strategic framework.

Telehealth has a role in the federally sponsored plan for health information technology (HIT) that encompasses electronic health records (EHRs) and the National Health Information Network (NHIN). The goals of telehealth and the national plan are complementary. One focuses on improving access to high quality health-care services and the other on the information systems to support those services. Telehealth needs the fully realized EHR to provide the best possible care when patients are geographically and chronologically separated from their providers. Some current telehealth projects are natural examples of how a distributed, accessible EHR such as that envisaged by the plan can be used to provide better care. The experiences of telehealth in organizing large networks of heterogeneous health-care entities can provide useful lessons as the process of implementing HIT moves forward.